MAT308 – Optimization

Module designation

Optimization (MAT308)

Semester(s) in which the module is taught

6^th

Person responsible for the module

Dr. Herry Suprajitno

Language

Indonesian

Relation to curriculum

Compulsory / elective / specialisation

Teaching methods

Lecture, lesson, discussion, and presentation.

Workload (incl. contact hours, self-study hours)

3×170 minutes (3×50 minutes lecture and lesson, 3×60 minutes structural activities, 3×60 minutes self-study) per week for 16 weeks

Credit points

3 CP (4,8 ECTS)

Required and recommended prerequisites for joining the module

Linear Programming (MAT203)

Multivariable Calculus (MAA203)

Numerical Method (MAT201)

Module objectives/intended learning outcomes

General Competence (Knowledge):

  Capable of implementing the optimization principle to the problems.

Specific Competence: student capable of

1.      Explaining optimization principles, convex sets characteristics and quadratic form

2.      Using appropriate method to solve the unconstraints problems

3.      Using appropriate method to solve the constraints problems

4.      Using Genetic Algorithm (GA) to solve a problem

5.      Using Simulated Annealing (SA) to solve a problem

6.      Using Ant Colony Optimization (ACO) to solve a problem

7.      Using GA, SA, ACO to solve a problem

Content

Introduction to optimization, unconstraints optimization, constraints   optimization, global optimization.

Examination forms

Essay and Presentation

Study and examination requirements

Students are considered to pass if they at least have got a final score 40 (D).

Final score is calculated as follow: 20% assignment + 10% softskill + 10% quiz + 25% UTS + 35% UAS (Presentation)

Final index is defined as follow:

Reading list

1.    Gen M dan Cheng R, 2000, Genetic Algorithms and Engineering Optimization, John Wiley & Sons, New York.

2.    Castro LN, 2006, Fundamentals of Natural Computing, Chapman & Hall, Boca Raton.

3.    Dorigo M dan Stutzle T, 2004, Ant Colony Optimization, MIT Press, Massachusetts

4.    Lazinica A, 2009, Particle Swarm Optimization, In-Tech, Vienna.

5.    Xin-She Yang, 2010, Firefly Algorithms for Multimodal Optimization, University of Cambridge.

6.    Karaboga  D. dan Akay, B., 2009, A Comparative Study of Artificial Bee Colony algorithm, Applied Mathematics and Computation, 214, 108–132

7.    Han J dan Kamber M, 2006, Data Mining Concepts and Techniques, 2^nd edition, Elsevier, Oxford.

8.    Prasetyo E, 2012, Data Mining Konsep dan Aplikasi Menggunakan Matlab, Penerbit Andi, Yogyakarta

9.    Stamp, M. dan Low, R M., 2007, Applied Cryptanalysis Breaking Ciphers in the Real World, John Wiley & Sons, New Jersey.